Traffic signal controllers typically collect data concerning traffic count, occupancy, and average speed. Current state of the art is for the signal controller to collect data, and bin or aggregate it into predefined time increments, for example fifteen minute or one-hour intervals. Traffic information available from the controller, then, is based strictly on these aggregations. Information on individual vehicles traveling through the intersection is not available.
Municipalities, other government agencies, and traffic researchers have long sought an efficient way to identify and analyze dangerous driving behavior in order to improve intersection safety (through signal timing, intersection re-engineering, or improved enforcement). Red light running and speeding are the behaviors most often researched.
Because traffic signal controllers present only aggregate data, it is impossible to identify events associated with individual vehicles. Further, vehicle data that are tracked (occupancy and count) do not have either time or signal status associated with them For example, vehicle counts and occupancy data do not include the dimension of signal status—when the light was red, green, or amber. The result is that it is impossible to analyze traffic signal controller data to determine the number, frequency, time phasing, or severity of dangerous driving behavior such as red light running and speeding. Severity can be determined by analyzing a combination of speed, acceleration, vehicle type, and intersection clearance time.
Current methods to analyze red light running include hand counts, video taping, and collecting data from automated enforcement systems. These methods suffer from several flaws: Hand counts are inaccurate, and liable to miss scenarios where multiple violations occur. Hand counts are impossible to validate—there is no empirical evidence of a violation. Further, hand counts typically collect only a fraction of the data required to fully analyze driver behavior. Hand counts cannot collect information relating to speed, acceleration, length into the red light cycle, or intersection clearance time. Video data collection improves only on accuracy of hand count, but not on the quality of related data (speed, acceleration, etc.).
Automated enforcement devices are capable of collecting much of the information needed for safety research. Red light enforcement systems operate in conjunction with traffic signal controllers. The enforcement device receives status input and uses vehicle detection devices to determine when a violation is occurring. Automated speed enforcement systems use laser or radar to calculate vehicle speed, and do not require a connection to a signal controller to determine a violation. In both cases however, data is typically collected only in the process of active enforcement. The data collected by such devices is directly affected by the visibility of the enforcement device to the drivers being monitored, much the same way the presence of a police cruiser at an intersection win have an effect on speeding and red light running.
Additionally, most, if not all, data analysis is done looking at single approaches to intersections with the remote (from the intersection) analysis of historical data. This precludes the collection and analysis of data from multiple vehicles and multiple approaches concurrently, providing results (to the traffic control system, for example) in near real time.
In conclusion, no traffic signal controller can identify and record dangerous driving behavior, and no automated enforcement device is capable of operating as a traffic signal controller and unobtrusively collecting intersection safety information. Further, because they cannot identify individual vehicle events, and capture information related to those events, current traffic signal controllers are not capable of operating as automated enforcement systems in addition to their function of traffic control.